Although workplace design and management are gaining more and more attention from modern organizations, workplace research is still very fragmented and spread across multiple disciplines in academia. There are several books on the market related to workplaces, facility management (FM), and corporate real estate management (CREM) disciplines, but few open up a theoretical and practical discussion across multiple theories from different fields of studies. Therefore, workplace researchers are not aware of all the angles from which workplace management and effects of workplace design on employees has been or could be studied. A lot of knowledge is lost between disciplines, and sadly, many insights do not reach workplace managers in practice. Therefore, this new book series is started by associate professor Rianne Appel-Meulenbroek (Eindhoven University of Technology, the Netherlands) and postdoc researcher Vitalija Danivska (Aalto University, Finland) as editors, published by Routledge. It is titled ‘Transdisciplinary Workplace Research and Management’ because it bundles important research insights from different disciplinary fields and shows its relevance for both academic workplace research and workplace management in practice. The books will address the complexity of the transdisciplinary angle necessary to solve ongoing workplace-related issues in practice, such as knowledge worker productivity, office use, and more strategic workplace management. In addition, the editors work towards further collaboration and integration of the necessary disciplines for further development of the workplace field in research and in practice. This book series is relevant for workplace experts both in academia and industry. This first book in the series focuses on the employee as a user of the work environment. The 21 theories discussed and applied to workplace design in this book address people’s ability to do their job and thrive in relation to the office workplace. Some focus more on explaining why people behave the way they do (the psychosocial environment), while others take the physical and/or digital workplace quality as a starting point to explain employee outcomes such as health, satisfaction, and performance. They all explain different aspects for achieving employee-workplace alignment (EWA) and thereby ensuring employee thriving. The final chapter describes a first step towards integrating these theories into an overall interdisciplinary framework for eventually developing a grand EWA theory. The Open Access version of this book, available at http://www.taylorfrancis.com/books/e/9781003128830, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
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Purpose: This article summarizes the shared principles and evidence underpinning methods employed in the three sentence-level (syntactic) grammatical intervention approaches developed by the authors. We discuss associated clinical resources and map a way forward for clinically useful research in this area. Method: We provide an overview of the principles and perspectives that are common across our three syntactic intervention approaches: MetaTaal (Zwitserlood, 2015; Zwitserlood, Wijnen, et al., 2015), the SHAPE CODING™ system (Ebbels, 2007; Ebbels et al., 2014, 2007), and Complex Sentence Intervention (Balthazar & Scott, 2017, 2018). A description of each approach provides examples and summarizes current evidence supporting effectiveness for children with developmental language disorder ranging in age from 5 to 16 years. We suggest promising directions for future research that will advance our understanding of effective practices and support more widespread adoption of syntactic interventions with school-age children. Conclusion: In each approach to syntactic intervention, careful and detailed analysis of grammatical knowledge is used to support target selection. Intervention targets are explicitly described and presented systematically using multimodal representations within engaging and functional activities. Treatment stimuli are varied within a target pattern in order to maximize learning. Similar intervention intervals and intensities have been studied and proven clinically feasible and have produced measurable effects. We identify a need for more research evidence to maximize the effectiveness of our grammatical interventions, encompassing languages other than English, as well as practical clinical tools to guide target selection, measurement of outcomes, and decisions about how to tailor interventions to individual needs.
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Retail industry consists of the establishment of selling consumer goods (i.e. technology, pharmaceuticals, food and beverages, apparels and accessories, home improvement etc.) and services (i.e. specialty and movies) to customers through multiple channels of distribution including both the traditional brickand-mortar and online retailing. Managing corporate reputation of retail companies is crucial as it has many advantages, for instance, it has been proven to impact generated revenues (Wang et al., 2016). But, in order to be able to manage corporate reputation, one has to be able to measure it, or, nowadays even better, listen to relevant social signals that are out there on the public web. One of the most extensive and widely used frameworks for measuring corporate reputation is through conducting elaborated surveys with respective stakeholders (Fombrun et al., 2015). This approach is valuable but deemed to be laborious and resource-heavy and will not allow to generate automatic alerts and quick and live insights that are extremely needed in this era of internet. For these purposes a social listening approach is needed that can be tailored to online data such as consumer reviews as the main data source. Online review datasets are a form of electronic Word-of-Mouth (WOM) that, when a data source is picked that is relevant to retail, commonly contain relevant information about customers’ perceptions regarding products (Pookulangara, 2011) and that are massively available. The algorithm that we have built in our application provides retailers with reputation scores for all variables that are deemed to be relevant to retail in the model of Fombrun et al. (2015). Examples of such variables for products and services are high quality, good value, stands behind, and meets customer needs. We propose a new set of subvariables with which these variables can be operationalized for retail in particular. Scores are being calculated using proportions of positive opinion pairs such as <fast, delivery> or <rude, staff> that have been designed per variable. With these important insights extracted, companies can act accordingly and proceed to improve their corporate reputation. It is important to emphasize that, once the design is complete and implemented, all processing can be performed completely automatic and unsupervised. The application makes use of a state of the art aspect-based sentiment analysis (ABSA) framework because of ABSA’s ability to generate sentiment scores for all relevant variables and aspects. Since most online data is in open form and we deliberately want to avoid labelling any data by human experts, the unsupervised aspectator algorithm has been picked. It employs a lexicon to calculate sentiment scores and uses syntactic dependency paths to discover candidate aspects (Bancken et al., 2014). We have applied our approach to a large number of online review datasets that we sampled from a list of 50 top global retailers according to National Retail Federation (2020), including both offline and online operation, and that we scraped from trustpilot, a public website that is well-known to retailers. The algorithm has carefully been evaluated by manually annotating a randomly sampled subset of the datasets for validation purposes by two independent annotators. The Kappa’s score on this subset was 80%.
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